Data Scientist

HOW YOU’LL CONTRIBUTE
• Develop and optimize ML infrastructure for fast serving of machine learning models in production environments, ensuring low-latency and high-throughput inference capabilities.
• Implement and maintain efficient ML flow processes, including model versioning, deployment, and monitoring to enable seamless transition from development to production.
• Collaborate with cross functional teams, data scientists, and other engineering disciplines to deploy ML models, ensure code quality, reproducibility, adherence to best practices in ML development, smooth integration with existing production systems, and create efficient data pipelines and data availability for ML modeling.
• Monitor and analyze model performance and accuracy over time, detecting and addressing model drift to maintain reliable and up-to-date ML systems.
• Stay up to date with the latest advancements in ML operations, deployment technologies, and monitoring frameworks through continuous learning and experimentation. Research, adopt, and promote best practices to enhance the efficiency and effectiveness of ML infrastructure.
• Document processes, best practices, and lessons learned to facilitate efficient ML operations and contribute to department/company knowledge base.
• Other duties as assigned.
• Required to perform duties outside of normal work hours based on business needs.
• Other duties as assigned

WHAT YOU’LL BRING

Required Education, Experience, Certification/Licensure
• Bachelor’s degree in computer science, software engineering, or a related field
• Advanced degree preferred.
• 2-5 years of related work experience in building machine learning platform solutions

KNOWLEDGE, SKILLS, AND ABILITIES (KSAs)
• Strong knowledge of software engineering principles and experience with programming languages such as Python.
• Strong problem-solving and analytical skills, with the ability to diagnose and address performance bottlenecks in ML systems.
• Excellent communication and collaboration skills to work effectively on cross-functional teams, and document processes and best practices.
• Strong understanding of ML models and algorithms in the areas of Large Language Models
• Experience with cloud computing platforms, such as AWS, GCP, or Azure
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes)
• Experience with IaC automation tools and scripts (e.g.,ML Flow, Air Flow, Terraform, Ansible, Puppet, etc.)
• Experience with Snowflake and cloud-based data pipelines.
• Experience with MLaaS platforms such as Azure ML, AWS Sagemaker and Databricks
• Proficient in version control and DevOps tools such as GitLab/GitHub/Azure DevOps
• Strong organizational or project management skills

Responsible for building and implementing deep learning-based transformer-based machine learning models in the areas of Natural Language processing (NLP) and Computer Vision (CV). Build and manage the infrastructure on cloud to deploy Machine Learning models in production in conformance with organization’s security and compliance needs. Experience in fine tuning Large Language Models (LLM’s) to task specific data sets. Deploy LLM and deep learning models in production and optimize real time inference on millions of predictions daily. This role can focus on R&D and/or Engineering responsibilities. R&D Role: Responsible for ML Operations with expertise in fast serving inference, ML flow, research, and development (R&D), and a strong focus on best practices in ML development and operations. Develop and maintain robust and efficient ML infrastructure, ensuring smooth ML flow from development to production, drive innovative R&D initiatives, and implement industry-leading best practices. Monitor and address model drift to ensure model performance and accuracy over time. Drive R&D initiatives to explore and implement innovative ML operations techniques, deployment technologies, and monitoring frameworks. Ensure efficient data pipelines and data availability for ML model serving and monitoring. Engineering Role: Build and manage the infrastructure on cloud (Azure or GCP or Databricks) to deploy Machine Learning models in production in conformance with organization’s security and compliance needs. Experience in Infrastructure as Code (IaC) automation and a strong background in software engineering and machine learning, as well as experience building and maintaining large-scale machine learning models in production. Implement and maintain model monitoring tools and processes to track key metrics, generate alerts, and facilitate proactive model maintenance. Work closely with cross-functional teams to identify and address infrastructure bottlenecks, optimize resource allocation, and improve scalability of ML systems.